Artificial intelligence (AI) provides machines with the ability to learn and respond the way humans do and is also referred to as machine learning. The step to building an AI system is to provide the data to learn from so that it can map relations between inputs and outputs and set up parameters such as “weights”/decision boundaries to predict responses for inputs in the future. Then, the model is tested on a second data set. This article outlines the promise this analytic approach has in medicine and cardiology.

Artificial intelligence (AI) provides machines with the ability to learn and respond the way humans do. It is a technique that trains a machine to assist humans in tasks that are based on human efforts and thinking and hence is also commonly referred to as machine learning. The ability to learn from past cases is the core of quantitative analysis and is being extensively used not only in medicine but also in business, engineering, social sciences, and many others. The machine “learns” how to “think” and make decisions based on the past data – almost as if it was human (Think: Google's Self-driving cars, iPhone's Siri).

With increasing data availability and data collection potential, combined with AI, mapping complex relations among several predictors and making decisions from huge amounts of data have never been simpler.

There are two objectives for analyzing data:[1] prediction of response for future inputs and information about the relationships between the input and response variables. Traditionally, statisticians have laid focus on the inference of the best-fit relationship between the inputs and outputs and asked the question “Is this input significant? If yes, then how?”

AI simply shifts the spotlight to the predictive accuracy of the data model. A data scientist (the AI – equivalent of a statistician) will, therefore, ask, “If I feed a new observation into my model, what the probability of my getting the correct classification label/continuous value for these inputs?” – even if the exact relationship between the inputs and responses is a bit of a black box.

While AI relies heavily on many concepts of statistics – k-means clustering, principal components analysis, and logistic regression, to name a few – it is not limited by the many assumptions that come bundled with each statistical model. With new kinds of carefully developed methods, AI is, therefore, capable of capturing complex patterns beyond concepts of linearity, continuation of boundary, etc., AI is also better suited to a data set which has a large number of independent variables (statistical models would be time-consuming/inaccurate) and a large number of observations (statistical models would overfit).

The first step to building an AI system is to provide the data to learn from or to “train” on (much like a statistical model would “fit” the data). The training period mostly comprises mapping relations between inputs and outputs and setting of model parameters such as “weights”/decision boundaries, etc., to equip the model to correct predict responses for unseen inputs in the future. Finally, the model is “tested” to check its performance on out-of-sample data. In some cases, “validation” is done to prevent overfitting (wherein the model would simply memorize the values and would perform very poorly on unseen inputs).

There are broadly three types of machine learning algorithms: supervised learning (during training, the model is given inputs, as well as their corresponding responses to train on, e.g., regression/classification), unsupervised learning (only inputs provided during training – clustering), and reinforcement learning (the model learns through trial and error and by rewarding its behavior using a feedback system).

Coming to the use of AI in medicine, health-care data can come from physical examination, readings from medical devices (including numeric/categorical data and images), demographics, laboratory data, etc. Cardiology, neurology, oncology, endocrinology, and urology are only some of the many specialties that have attracted AI research.

Early-stage diagnosis of malignant melanoma [2] based on the classification of dermoscopy images has been effectively done using machine-learning techniques. Catto et al.[3] have compared accuracies of conventional statistics to two AI models – artificial neural networks and neuro-fuzzy modeling, in predicting the presence of bladder cancer and timing of relapse. Enshaei et al.[4] used an AI system to predict overall survival and surgical outcome in epithelial ovarian cancer patients, using a huge data set of routine clinical data and survival data, collected over 10 years. AI has also been used for more effective prediction of postoperative complications for patients undergoing anterior cervical discectomy and fusion.[5] Hirschauer et al.[6] demonstrated early prediction of Parkinson's disease using motor, nonmotor features, and neuroimages as inputs.

Recently, Dawes et al.[7] first created a three-dimensional (3D) model of right ventricular motion from cine magnetic resonance imaging images and created an algorithm of 3D patterns of systolic cardiac motion that could predict outcome in patients with pulmonary hypertension with high accuracy. They combined the motion parameters with clinical, functional, and hemodynamic markers, all of which they fed into an AI model to predict survival and right ventricular failure in pulmonary hypertension. Playford et al.[8] developed an AI system to correctly classify between atrial fibrillation and sinus rhythm by training the model on a database of electrocardiography (ECG) obtained from an ECG monitoring device. Oskouie et al.[9] have classified heart failure with preserved ejection fraction patients into three distinct phenotypic subgroups and then extracted trends from within the subgroups, for example, finding prevalence of a specific age group or some particular biomarkers within a subgroup.

Data are important in clinical medicine today, and as shown above, AI techniques are a way to handle the vast volumes of data coming from patients. Statistical techniques depend on the variables given to a tool and then predict an outcome, whereas AI techniques have the potential of being able to take in innumerable variables from patient databases and improve the diagnostic accuracy of the prognostication. Reading of various imaging tests/pathology interpretations can also improve, for instance, beyond just simple interpretations as the digitized images can be directly fed into algorithms and quantification can be used for image interpretation. Properly applied algorithms for physiological data such as ECGs and hemodynamic data will gradually gain importance, especially in the management of critical patients.

Future issues of the journal will contain detailed explanations of the two most popular AI algorithms currently being employed in health care – artificial neural networks and support vector machines.